Python Machine Learning Bootcamp: Course Description
This step-by-step course will start with regressions, one of the most common building blocks of machine learning. Then you’ll learn about different ways to approach machine learning like k-nearest neighbors, decision trees, and random forest.
Learn key statistical concepts including bias, various, and overfitting, and how to measure the accuracy of your models. Statistics is the foundation of many topics in machine learning and you'll learn to apply those concepts to machine learning.
Use Python's sci-kit learn library to do a variety of machine learning algorithms. Learn different types of machine learning algorithms and when you should use them. Create your own models, test them, and evaluate the performance with hands-on projects.
Learn to solve real-world problems by working on real projects in this hands-on course and be ready to take your new skills into the real world.
Prerequisites: This course does require students to be comfortable with Python and its data science libraries (NumPy and Pandas)
This course is delivered in partnership with Noble Desktop, a leading design and coding school in NYC with over three decades of experience.
Python Machine Learning Bootcamp Course Dates
All classes are led by a live instructor. Class times listed are Eastern time.
Python Machine Learning Bootcamp: Full Overview
Fundamentals
- Basic Regression Analysis
- Advanced Regression Analysis
Classification
- Logistic Regression
- K-nearest Neighbors
Decision Trees
- Decision Trees
- Random forest
This course is meant for beginners and requires no prior coding experience.
You will receive a training manual created by our experts for course review.
Free retakes
Detailed curriculum
Live instructor
Flexible rescheduling